Physics-inspired transformer quantum states via latent imaginary-time evolution
This work addresses the need for more interpretable and efficient neural quantum states in quantum physics simulations, representing an incremental improvement with a novel architectural twist.
The authors tackled the problem of designing more physically transparent and compact neural quantum states by reinterpreting them as approximations to latent imaginary-time evolution, resulting in physics-inspired transformer quantum states that achieve accuracies comparable to or exceeding state-of-the-art methods with substantially fewer variational parameters for the frustrated J1-J2 Heisenberg model.
Neural quantum states (NQS) are powerful ansätze in the variational Monte Carlo framework, yet their architectures are often treated as black boxes. We propose a physically transparent framework in which NQS are treated as neural approximations to latent imaginary-time evolution. This viewpoint suggests that standard Transformer-based NQS (TQS) architectures correspond to physically unmotivated effective Hamiltonians dependent on imaginary time in a latent space. Building on this interpretation, we introduce physics-inspired transformer quantum states (PITQS), which enforce a static effective Hamiltonian by sharing weights across layers and improve propagation accuracy via Trotter-Suzuki decompositions without increasing the number of variational parameters. For the frustrated $J_1$-$J_2$ Heisenberg model, our ansätze achieve accuracies comparable to or exceeding state-of-the-art TQS while using substantially fewer variational parameters. This study demonstrates that reinterpreting the deep network structure as a latent cooling process enables a more physically grounded, systematic, and compact design, thereby bridging the gap between black-box expressivity and physically transparent construction.